k_clique_communities(G, k, cliques=None) 利用渗透法在图中寻找K族群落。 K集团社区是所有K规模的集团的联盟,可以通过邻近的(共享K-1节点)K集团…
k-clique communitiesconnected components中文摘要 偵測真實網路中的community結構是重要的研究主題,在許多方面皆有所應用,譬如:生物學,社會學,全球資訊網以及行動通信網路等.有許多不同的community偵測方法被提出.其中,我們發現Palla, G., et al提出的k-clique community相當重要.不過,他們的程式CFinder執行時間比起...
With this measure, the problem of identifying the communities reduces to finding the partitioning that maximises the modularity. 译文:有了这种方法,识别社区的问题就简化为找到最大化模块化的分区。 The incidence graph is a bipartite graph where the sets of vertices represent the variables and ...
文献《1》中提到,一般网络的Q值在0.3 ~ 0.7之间,能够说明很好的community structure。 回过头来看看Modularity的计算方式,会发现它对于community的衡量比早期的一些标准(例如k-clique)要简单很多。它并没有要求community内部具有某种结构,也不要求community中的每个node达到某种标准,而只统计community中内部和外部的边的总数。
The computational time complexity of LWP locally optimized method is O(K2d), where ‘K’ denotes number of vertices in sub-network to be discovered and ‘d’ denotes average degree of vertices in sub-network. A maximum clique extension based local community detection method, known as LCD-MC,...
import spectral_clustering adj_matrix = [...] communities = spectral_clustering(adj_matrix, k...
when a node meets another node, it must decide (1) whether to include this encountered node into the familiar set and the local community, and (2) whether to merge the local communities of these two nodes. The difference among SIMPLE, K-CLIQUE, and MODULARITY lies in the admission criteria...
3.1.1.1 Kernighan & Lin algorithm - K-L算法 K-L(Kernighan-Lin)算法是一种将已知网络划分为已知大小的两个社区的二分方法,它是一种贪婪算法。它的主要思想是为网络划分定义了一个函数增益Q,Q表示的是社区内部的边数与社区之间的边数之差,根据这个方法找出使增益函数Q的值成为最大值的划分社区的方法。
fromcommunities.algorithmsimportspectral_clusteringadj_matrix=[...]communities=spectral_clustering(adj_matrix,k=5) Bron-Kerbosch algorithm bron_kerbosch(adj_matrix : numpy.ndarray, pivot : bool = False) -> list Implementation of theBron-Kerbosch algorithmfor maximal clique detection. A maximal clique...
Although k-core and communities are two ways of decomposing the same network, there may be overlaps or intricate relationships between them. In the present paper, we study the relation between the k-core decomposition and the community structures of several empirical and synthetic networks. In part...